title: A microRNA-based prediction algorithm for diagnosis of non-small lung cell carcinoma in minimal biopsy material.
abstract: BACKGROUND
  Diagnosis is jeopardised when limited biopsy material is available or histological quality compromised. Here we developed and validated a prediction algorithm based on microRNA (miRNA) expression that can assist clinical diagnosis of lung cancer in minimal biopsy material to improve clinical management.
  
  
  METHODS
  Discovery utilised Taqman Low Density Arrays (754 miRNAs) in 20 non-small cell lung cancer (NSCLC) tumour/normal pairs. In an independent set of 40 NSCLC patients, 28 miRNA targets were validated using qRT-PCR. A prediction algorithm based on eight miRNA targets was validated blindly in a third independent set of 47 NSCLC patients. The panel was also tested in formalin-fixed paraffin-embedded (FFPE) specimens from 20 NSCLC patients. The genomic methylation status of highly deregulated miRNAs was investigated by pyrosequencing.
  
  
  RESULTS
  In the final, frozen validation set the panel had very high sensitivity (97.5%), specificity (96.3%) and ROC-AUC (0.99, P=10(-15)). The panel provided 100% sensitivity and 95% specificity in FFPE tissue (ROC-AUC=0.97 (P=10(-6))). DNA methylation abnormalities contribute little to the deregulation of the miRNAs tested.
  
  
  CONCLUSIONS
  The developed prediction algorithm is a valuable potential biomarker for assisting lung cancer diagnosis in minimal biopsy material. A prospective validation is required to measure the enhancement of diagnostic accuracy of our current clinical practice.
authors.affiliation: 1] Roy Castle Lung Cancer Research programme, Department of Molecular and Clinical Cancer Medicine, Institute of Translational Medicine, University of Liverpool, Liverpool, UK [2] BIOMICs Research Group, University of the Basque Country, Vitoria, Spain.
authors.forename: N G
authors.initials: NG
authors.lastname: Bediaga
authors.forename: M P A
authors.initials: MP
authors.lastname: Davies
authors.forename: A
authors.initials: A
authors.lastname: Acha-Sagredo
authors.forename: R
authors.initials: R
authors.lastname: Hyde
authors.forename: O Y
authors.initials: OY
authors.lastname: Raji
authors.forename: R
authors.initials: R
authors.lastname: Page
authors.forename: M
authors.initials: M
authors.lastname: Walshaw
authors.forename: J
authors.initials: J
authors.lastname: Gosney
authors.forename: A
authors.initials: A
authors.lastname: Alfirevic
authors.forename: J K
authors.initials: JK
authors.lastname: Field
authors.forename: T
authors.initials: T
authors.lastname: Liloglou
mesh_terms: D000368:Aged; D000465:Algorithms; D014408:Biomarkers, Tumor / Q000235:genetics; D001706:Biopsy; D002289:Carcinoma, Non-Small-Cell Lung / Q000175:diagnosis* / Q000235:genetics* / Q000473:pathology; D019175:DNA Methylation; D005260:Female; D015870:Gene Expression; D006801:Humans; D008175:Lung Neoplasms / Q000175:diagnosis* / Q000235:genetics* / Q000473:pathology; D008297:Male; D035683:MicroRNAs / Q000235:genetics*; D008954:Models, Biological; D015233:Models, Statistical; D016612:Paraffin Embedding
chemical_list: D014408:Biomarkers, Tumor; D035683:MicroRNAs